The present invention relates to techniques for improving error rate performance in recording devices and, more particularly, to optimizing detector target polynomials in read/write channels to achieve best error rate performance in disk drives.
In an apparatus for recording and reproducing data in/from recording media, reproduction of data may be carried out by low-pass filtering, and then equalization, followed by data detection, on noisy read-back waveforms. While a low-pass filter cuts off excessive out-of-band noise, an equalizer attempts to shape the waveforms to certain prescribed response so that a data detector can detect data bits from the equalized waveforms with reasonable implementation complexity. The prescribed response is often called equalization target polynomial, or detector target polynomial. For a given equalization target polynomial, the equalizer attempts to minimize the differences, often called errors, between the equalized waveforms and estimated ideal waveforms produced by the convolution of bit sequences produced by data detector and equalization target polynomial. Since the read-back waveforms are often noisy, the errors may contain both noise and mis-equalization errors between the equalized noiseless waveforms and estimated ideal waveforms. The errors have adverse impact on error rate performance of data detector. Different equalization target polynomials result in different errors, and hence different error rate performance of detector. A desirable equalization target polynomial is the one which minimizes the power of errors. Once an equalization target polynomial is chosen, an equalizer can be designed to shape the read-back waveforms to the target response.
In the case where the original data recorded on, for example, a recording disk medium is reproduced by means of a pickup head, there is noise in a waveform of the reproduced signal output of the head. As a method of shaping read-back waveform to certain equalization target polynomial, there has been conventionally used an adaptive equalizer utilizing a finite impulse response (referred to as “FIR”, hereinafter) filter. Since different equalization target polynomials result in different error rates, an optimal equalization target polynomial needs to be chosen so that the best error rate performance of read-back waveform may be achieved.
Particularly in recent years, the data recording density on the recording medium has been remarkably increased and inter-symbol interface (ISI) between the recorded data on the medium is increased, and also a noise influence in a data transmission path cannot be ignored because of reduction in amplitude of the reproduction signal. In order to improve the signal reading efficiency with reduction of a bit error rate of recorded or playback waveforms, a playback data detecting method has been employed to detect an optimal playback data by operating a partial response (referred to as “PR”, hereinafter) equalization of an automatic adaptive equalizer in combination with a Viterbi decoding unit, whereby data stream of transmission signals is monitored before and after a specified time point so as to select the most likely data pattern closest to a desired data pattern among from the monitored data patterns to thereby obtain the optimal performance of data detection. Various adaptive techniques have been proposed to provide a PR equalization with improved reliability. See, e.g., U.S. Patent Publication No. 2003/0123364 A1 and U.S. Pat. No. 6,385,239. Other examples of adaptive filtering techniques are found in U.S. Patent Publication No. 2003/0031242 A1; and U.S. Pat. Nos. 6,754,340 and 6,137,881.
In disk drive systems, detector target polynomials play a major role in determining the read channel error rate performance. Different detector target polynomials are needed for different heads/media and operation conditions to provide proper shaping to read back signal. An example of a polynomial is [1, a, b, 0, −b, −a, −1], and a target polynomial means any choice of the polynomial with certain constraints. The ideal sample values are the result of convolution of the detector target polynomial with a given input binary sequence. For example, for partial response channel PR4 with target polynomial [1, 0, −1], the all possible ideal sample values are 2 or 0 or −2.
Detector target polynomials, also known as equalization target polynomials, need to be optimized in order to yield the best error rate performance. There could be abundant choices of possible detector target polynomials for use in channels. It is generally not practical to do a brute force search for optimal targets amount all possible targets due to limited time and other practical constraints. A detector target polynomial is thus typically chosen either empirically or by trials of only a handful of targets.